Related papers: Disk Wind Feedback from High-mass Protostars. V. A…
The formation of stars is usually accompanied by the launching of protostellar outflows. Observations with the Atacama Large Millimetre/sub-millimetre Array (ALMA) will soon revolutionalise our understanding of the morphologies and…
To test theoretical models of massive star formation it is important to compare their predictions with observed systems. To this end, we conduct CO molecular line radiative transfer post-processing of 3D magneto-hydrodynamic (MHD)…
We perform a sequence of 3D magnetohydrodynamic (MHD) simulations of the outflow-core interaction for a massive protostar forming via collapse of an initial cloud core of $60~{M_\odot}$. This allows us to characterize the properties of disk…
Recent high-resolution observations have shown stellar winds to harbour complexities which strongly deviate from spherical symmetry, generally assumed as standard wind model. One such morphology is the archimedean spiral, generally believed…
With the aim of understanding the role of outflows in star formation, we performed a statistical study of the physical parameters of outflows in eleven massive protoclusters associated with ultra-compact HII regions. A total of 106 outflow…
Star formation is ubiquitously associated with the ejection of accretion-powered outflows that carve bipolar cavities through the infalling envelope. This feedback is expected to be important for regulating the efficiency of star formation…
Protostellar outflows provide a means to probe the accretion process of forming stars and their ability to inject energy into their surroundings. However, conclusions based on outflow observations depend upon the degree of accuracy with…
Magnetic stresses collimate protostellar winds into a common distribution of force with angle. Sweeping into the ambient medium, such winds drive bipolar molecular outflows whose properties are insensitive to the distribution of ambient gas…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to identify protostellar outflows in molecular line spectra. We conduct magneto-hydrodynamics simulations that model forming stars that launch…
Stellar astrophysics relies on diverse observational modalities-primarily photometric light curves and spectroscopic data from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within…
Magnetohydrodynamic disk-winds play a key role in the formation of massive stars by providing the fine-tuning between accretion and ejection, where excess angular momentum is redirected away from the disk, allowing further mass growth.…
We present a statistical study on the orientation of outflows with respect to large-scale filaments and the magnetic fields. Although filaments are widely observed toward Galactic star-forming regions, the exact role of filaments in star…
The role of outflows in the formation of stars and the protostellar disks that generate them is a central question in astrophysics. Outflows are associated with star formation across the entire stellar mass spectrum. In this review, we…
Protoplanetary disks are the sites of vigorous hydrodynamic processes, such as accretion and outflows, and ultimately establish the conditions for the formation of planets. The properties of disk outflows are often inferred through analysis…
The driving mechanism of massive outflows observed in high-mass star-forming regions is investigated using three-dimensional magnetohydrodynamics (MHD) and protostellar evolution calculations. In our previous paper, we showed that the mass…
Understanding the formation pathway for close-companion protostars is central to unraveling the processes that govern stellar multiplicity and very early star formation. We analyze a large sample of 51 Class 0/I close-companion protostellar…
End-to-end deep learning models fed with multi-band galaxy images are powerful data-driven tools used to estimate galaxy physical properties in the absence of spectroscopy. However, due to a lack of interpretability and the associational…
Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal…
Intermediate mass protostarsprovide a bridge between theories of low- and high-mass star formation. Emerging molecular outflows can be used to determine the influence of fragmentation and multiplicity on protostellar evolution through the…